Load the library
Load the dataset
Plot1(a): Trend on people who tested positive (For all the data).
#Plot to visualise trend on positive cases.
plot_positive_case <- trend_hb_daily %>%
filter (hb_name == "Scotland") %>%
ggplot()+
aes(x = date, y = daily_positive)+
geom_line()+
scale_x_date(breaks = "1 month", date_labels = "%b - %y" )+
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))+
ggtitle("People tested positive") +
xlab("Date (Months)") +
ylab("No of Positive Cases") +
color_theme()
ggplotly(plot_positive_case)
NA
NA
Plot1(b): Trend on people who tested positive(For Past 6 months).
plot_positive_case <- trend_hb_daily %>%
filter(date >="2021-04-01") %>%
filter (hb_name == "Scotland") %>%
ggplot()+
aes(x = date, y = daily_positive)+
geom_line()+
#scale_x_date(breaks = "1 month")+
scale_x_date(breaks = "1 month", date_labels = "%b - %y" )+
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))+
ggtitle("People tested Positive") +
xlab("Date (Months)") +
ylab("No of Positive Cases") +
color_theme()
ggplotly(plot_positive_case)
Plot1(c): Total Positive Case by neighborhood (Based on NHS Health board).
trend_hb_daily_tot <- trend_hb_daily %>%
filter(hb_name != "Scotland") %>%
group_by(hb) %>%
summarise(total_count = sum(daily_positive))
map_data <- zones_nhs_hb %>%
inner_join(trend_hb_daily_tot, by = c("code" = "hb"))
map_palette <- colorNumeric("viridis", domain = range(map_data$total_count))
map_data <- map_data %>%
arrange(total_count)
map_data <- map_data %>%
mutate(colour = map_palette(total_count))
map_data %>%
leaflet() %>%
addPolygons(
popup = ~ str_c(name, "<br>", "Positive Case = ", total_count, sep = ""),
color = ~colour
) %>%
addLegend(
position = "bottomright",
colors = ~colour,
labels = ~name
)
NA
Plot1(c): Total Positive Case by neighborhood (Based on Local Authority).
trend_la_daily_tot <- trend_la_daily %>%
group_by(ca) %>%
summarise(total_count = sum(daily_positive))
map_data <- zones_la %>%
inner_join(trend_la_daily_tot, by = c("code" = "ca"))
map_palette <- colorNumeric("viridis", domain = range(map_data$total_count))
map_data <- map_data %>%
arrange(total_count)
map_data <- map_data %>%
mutate(colour = map_palette(total_count))
map_data %>%
leaflet() %>%
addPolygons(
popup = ~ str_c(name, "<br>", "Positive Case = ", total_count, sep = ""),
color = ~colour
) %>%
addLegend(
position = "bottomright",
colors = ~colour,
labels = ~name
)
Check if the forecast data fits our existing data
# Set the training data from Jan to Aug
train <- trend %>%
filter_index("2021-01-01" ~ "2021-08-31")
# run the model on the training set
fit_train <- train %>%
model(
mean_model = MEAN(daily_positive),
arima = ARIMA(daily_positive),
snaive = SNAIVE(daily_positive)
)
forecast_test <- fit_train %>%
forecast(h = 30)
forecast_test %>%
autoplot(train, level = NULL) +
autolayer(filter_index(trend, "2021-09-01" ~.), color = "black") +
ggtitle("Forecasts for positive cases") +
xlab("Date") + ylab("Daily Positive") +
guides(colour=guide_legend(title="Forecast"))
Plot variable not specified, automatically selected `.vars = daily_positive`

Check for the accuracy of the model
The minimum RMSE is chosen.
accuracy_model <- accuracy(forecast_test, trend)
accuracy_model %>%
select(-.type) %>%
arrange(RMSE)
2 Analyse the trend on Hospitalisations:
plot_hosp <- trend_hb_daily %>%
filter(date >="2021-04-01") %>%
filter (hb_name == "Scotland") %>%
filter(!(is.na(hospital_admissions))) %>%
ggplot()+
aes(x = date, y = hospital_admissions)+
geom_col()+
scale_x_date(breaks = "1 week", date_labels = "%d - %b" )+
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))+
ggtitle("Trend on Hospitalisations") +
xlab("Date (months)") +
ylab("No of patients admitted") +
color_theme()
ggplotly(plot_hosp)
NA
2 (b) Forecast on Hospitalisation:**
Using 6 months of data ( Two - Wave)
trend_hosp <- trend_hb_daily %>%
filter (hb_name == "Scotland") %>%
filter(date >="2021-06-01") %>%
filter(!(is.na(hospital_admissions))) %>%
select(date, hospital_admissions)
trend_hosp <- as_tsibble(trend_hosp, index = date)
fit <- trend_hosp %>%
model(
snaive = SNAIVE(hospital_admissions),
mean_model = MEAN(hospital_admissions),
arima = ARIMA(hospital_admissions),
ets = ETS(log(hospital_admissions) ~ error("M") + trend("Ad") + season("A"))
)
forecast_14days <- fit %>%
forecast(h = 14)
forecast_14days %>%
# filter(.model == "snaive") %>%
autoplot(trend_hosp, level = NULL) +
ggtitle("Forecasts for Hospital admissions cases for 2 weeks") +
xlab("Year") +
ylab("No of patients admitted") +
guides(colour = guide_legend(title = "Forecast"))+
scale_x_date(breaks = "1 month", date_labels = "%B" )+
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))

forecast_1month <- fit %>%
fabletools::forecast(h = "1 month")
forecast_1month %>%
filter(.model %in% c("snaive","arima")) %>%
autoplot(trend_hosp, level = NULL) +
ggtitle("Forecasts for Hospitalisation for one month") +
xlab("Year") +
ylab("No of Positive Cases") +
guides(colour = guide_legend(title = "Forecast"))+
scale_x_date(breaks = "1 month", date_labels = "%B" )+
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))

# set the training data
train <- trend_hosp %>%
filter_index("2021-06-01" ~ "2021-08-31")
# run the model on the training set
fit_train <- train %>%
model(
mean_model = MEAN(hospital_admissions),
arima = ARIMA(hospital_admissions),
snaive = SNAIVE(hospital_admissions),
ets = ETS(log(hospital_admissions) ~ error("M") + trend("Ad") + season("A"))
)
forecast_test <- fit_train %>%
forecast(h = 35)
forecast_test %>%
filter(.model %in% c("arima","snaive")) %>%
autoplot(train ) +
autolayer(filter_index(trend_hosp, "2021-06-01" ~.), color = "black") +
ggtitle("Forecasts for Hospitalisations") +
facet_wrap(~.model)+
xlab("Date") +
ylab("No of Patients admitted") +
guides(colour=guide_legend(title="Forecast"))
Plot variable not specified, automatically selected `.vars = hospital_admissions`

accuracy_model <- accuracy(forecast_test, trend_hosp)
Warning: The future dataset is incomplete, incomplete out-of-sample data will be treated as missing.
1 observation is missing at 2021-10-05
accuracy_model %>%
select(-.type) %>%
arrange(RMSE)
Plot3(a): Trend on people who tested positive (For all the data).
#Plot to visualize trend on positive cases.
plot_vaccine <- daily_vacc_hb_plot %>%
ggplot()+
aes(x = date, y = number_vaccinated)+
geom_line(aes(color = dose))+
facet_wrap(~dose)+
scale_x_date(breaks = "1 month", date_labels = "%b - %y" )+
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))+
ggtitle("Trend on Vaccination") +
xlab("Year") +
ylab("No of Positive Cases") +
color_theme()
ggplotly(plot_vaccine)
---
title: "PHS_Covid Analysis"
output: html_notebook
---

### **Load the library**

```{r message=FALSE, warning=FALSE, include=FALSE}
library(here)
library(ggplot2)
library(plotly)
library(fable)
library(fabletools)
library(forecast)
library(tsibble)
library(tsibbledata)
library(RColorBrewer)
library(leaflet)
library(tmap)
```

### **Load the dataset**

```{r message=FALSE, warning=FALSE, include=FALSE}
#Load the data------------------------------------------------------------------
source(here("cleaning_scripts/phs_covid_cleaning.R"))
```

### ***Plot1(a): Trend on people who tested positive (For all the data).***

```{r}
#Plot to visualise trend on positive cases.
plot_positive_case <- trend_hb_daily %>% 
  filter (hb_name == "Scotland") %>% 
  ggplot()+
  aes(x = date, y = daily_positive)+
  geom_line()+
  scale_x_date(breaks = "1 month", date_labels = "%b - %y" )+
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))+
  ggtitle("People tested positive") +
  xlab("Date (Months)") +
  ylab("No of Positive Cases") +
  color_theme() 

ggplotly(plot_positive_case)


```

### ***Plot1(b): Trend on people who tested positive(For Past 6 months).***

```{r}
plot_positive_case <- trend_hb_daily %>% 
  filter(date >="2021-04-01") %>% 
  filter (hb_name == "Scotland") %>% 
  ggplot()+
  aes(x = date, y = daily_positive)+
  geom_line()+
  #scale_x_date(breaks = "1 month")+
  scale_x_date(breaks = "1 month", date_labels = "%b - %y" )+
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))+
  ggtitle("People tested Positive") +
  xlab("Date (Months)") +
  ylab("No of Positive Cases") +
  color_theme()

ggplotly(plot_positive_case)
```

### ***Plot1(c): Total Positive Case by neighborhood (Based on NHS Health board).***


```{r}

trend_hb_daily_tot <- trend_hb_daily %>% 
  filter(hb_name != "Scotland") %>% 
  group_by(hb) %>% 
  summarise(total_count = sum(daily_positive))
  

map_data <- zones_nhs_hb %>% 
    inner_join(trend_hb_daily_tot, by = c("code" = "hb"))
  
map_palette <- colorNumeric("viridis", domain = range(map_data$total_count))

map_data <- map_data %>% 
  arrange(total_count)

 map_data <- map_data %>% 
    mutate(colour = map_palette(total_count))
 
  map_data %>%
    leaflet() %>%
    addPolygons(
      popup = ~ str_c(name, "<br>",  "Positive Case = ", total_count, sep = ""),
      color = ~colour
    ) %>%
    addLegend(
      position = "bottomright",
      colors = ~colour,
      labels = ~name
    )

```


### ***Plot1(c): Total Positive Case by neighborhood (Based on Local Authority).***


```{r}
trend_la_daily_tot <- trend_la_daily %>% 
  group_by(ca) %>% 
  summarise(total_count = sum(daily_positive))

map_data <- zones_la %>% 
    inner_join(trend_la_daily_tot, by = c("code" = "ca"))
  
map_palette <- colorNumeric("viridis", domain = range(map_data$total_count))

map_data <- map_data %>% 
  arrange(total_count)

 map_data <- map_data %>% 
    mutate(colour = map_palette(total_count))
 
map_data %>%
    leaflet() %>%
    addPolygons(
      popup = ~ str_c(name, "<br>",  "Positive Case = ", total_count, sep = ""),
      color = ~colour
    ) %>%
    addLegend(
      position = "bottomright",
      colors = ~colour,
      labels = ~name
    )
```




## **EXTRA 1: Forecast on Positive cases:**

```{r}
# The data is chosen from Jun as it is the beginning of wave 1 
trend <- trend_hb_daily %>% 
  filter (hb_name == "Scotland") %>% 
  filter(date >="2021-06-01") %>% 
  select(date, daily_positive)

# Data is converted into timeseries for forecasting
trend <- as_tsibble(trend, index = date)

# Different forecasting methods are used 
fit <- trend %>%
  model(
    snaive = SNAIVE(daily_positive),
    mean_model = MEAN(daily_positive),
    arima = ARIMA(daily_positive)
  )

# Forecast for 2 weeks (14 days)
forecast_14days <- fit %>%
  forecast(h = 14)
forecast_14days
```

```{r}
forecast_14days %>%
 #filter(.model == "snaive") %>%
 autoplot(trend, level = NULL) +
  ggtitle("Forecasts for Positive cases for 2 weeks") +
  xlab("Year") +
  ylab("No of Positive Cases") +
  guides(colour = guide_legend(title = "Forecast"))+
  scale_x_date(breaks = "1 month", date_labels = "%B" )+
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
```

**EXTRA: Forecast for one month:**
```{r}
forecast_1month <- fit %>%
  fabletools::forecast(h = "1 month")

forecast_1month %>%
 #filter(.model == "snaive") %>%
 autoplot(trend, level = NULL) +
  ggtitle("Forecasts for Positive cases for one month") +
  xlab("Year") +
  ylab("No of Positive Cases") +
  guides(colour = guide_legend(title = "Forecast"))+
  scale_x_date(breaks = "1 month", date_labels = "%B" )+
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))

```

## **Check if the forecast data fits our existing data**

```{r}

# Set the training data from Jan to Aug
train <- trend %>%
  filter_index("2021-01-01" ~ "2021-08-31")

# run the model on the training set 
fit_train <- train %>%
  model(
    mean_model = MEAN(daily_positive),
    arima = ARIMA(daily_positive),
    snaive = SNAIVE(daily_positive)
  )

forecast_test <- fit_train %>% 
  forecast(h = 30)

forecast_test %>%
  autoplot(train, level = NULL) +
    autolayer(filter_index(trend, "2021-09-01" ~.), color = "black") +
    ggtitle("Forecasts for positive cases") +
    xlab("Date") + ylab("Daily Positive") +
    guides(colour=guide_legend(title="Forecast"))

```

## **Check for the accuracy of the model**

The minimum RMSE is chosen.
```{r}
accuracy_model <- accuracy(forecast_test, trend)

accuracy_model %>% 
  select(-.type) %>%
  arrange(RMSE)
```

## **2 Analyse the trend on Hospitalisations:**

```{r}
plot_hosp <- trend_hb_daily %>% 
  filter(date >="2021-04-01") %>% 
  filter (hb_name == "Scotland") %>% 
  filter(!(is.na(hospital_admissions))) %>% 
  ggplot()+
  aes(x = date, y = hospital_admissions)+
  geom_col()+
  scale_x_date(breaks = "1 week", date_labels = "%d - %b" )+
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))+
  ggtitle("Trend on Hospitalisations") +
  xlab("Date (months)") +
  ylab("No of patients admitted") +
  color_theme()

ggplotly(plot_hosp)

```


## 2 (b) Forecast on Hospitalisation:**

Using 6 months of data ( Two - Wave)

```{r}
trend_hosp <- trend_hb_daily %>% 
  filter (hb_name == "Scotland") %>% 
  filter(date >="2021-06-01") %>% 
  filter(!(is.na(hospital_admissions))) %>% 
  select(date, hospital_admissions)

trend_hosp <- as_tsibble(trend_hosp, index = date)

fit <- trend_hosp %>%
  model(
    snaive = SNAIVE(hospital_admissions),
    mean_model = MEAN(hospital_admissions),
    arima = ARIMA(hospital_admissions),
    ets = ETS(log(hospital_admissions) ~ error("M") + trend("Ad") + season("A"))
  )

forecast_14days <- fit %>%
  forecast(h = 14)

forecast_14days %>%
# filter(.model == "snaive") %>%
 autoplot(trend_hosp, level = NULL) +
  ggtitle("Forecasts for Hospital admissions cases for 2 weeks") +
  xlab("Year") +
  ylab("No of patients admitted") +
  guides(colour = guide_legend(title = "Forecast"))+
  scale_x_date(breaks = "1 month", date_labels = "%B" )+
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))

```


```{r}
forecast_1month <- fit %>%
  fabletools::forecast(h = "1 month")

forecast_1month %>%
 filter(.model %in% c("snaive","arima")) %>%
 autoplot(trend_hosp, level = NULL) +
  ggtitle("Forecasts for Hospitalisation for one month") +
  xlab("Year") +
  ylab("No of Positive Cases") +
  guides(colour = guide_legend(title = "Forecast"))+
  scale_x_date(breaks = "1 month", date_labels = "%B" )+
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))

```

```{r}
#  set the training data
train <- trend_hosp %>%
  filter_index("2021-06-01" ~ "2021-08-31")

# run the model on the training set 
fit_train <- train %>%
  model(
    mean_model = MEAN(hospital_admissions),
    arima = ARIMA(hospital_admissions),
    snaive = SNAIVE(hospital_admissions),
    ets = ETS(log(hospital_admissions) ~ error("M") + trend("Ad") + season("A"))
  )

forecast_test <- fit_train %>% 
  forecast(h = 35)

forecast_test %>%
  filter(.model %in% c("arima","snaive")) %>%
  autoplot(train ) +
    autolayer(filter_index(trend_hosp, "2021-06-01" ~.), color = "black") +
    ggtitle("Forecasts for Hospitalisations") +
    facet_wrap(~.model)+
    xlab("Date") + 
    ylab("No of Patients admitted") +
    guides(colour=guide_legend(title="Forecast"))

```



```{r}
accuracy_model <- accuracy(forecast_test, trend_hosp)

accuracy_model %>% 
  select(-.type) %>%
  arrange(RMSE)
```

### ***Plot3(a): Trend on people who tested positive (For all the data).***

```{r}
#Plot to visualize trend on positive cases.
plot_vaccine <- daily_vacc_hb_plot %>% 
  ggplot()+
  aes(x = date, y = number_vaccinated)+
  geom_line(aes(color = dose))+
  facet_wrap(~dose)+
  scale_x_date(breaks = "1 month", date_labels = "%b - %y" )+
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))+
  ggtitle("Trend on Vaccination") +
  xlab("Year") +
  ylab("No of Positive Cases") +
  color_theme()

ggplotly(plot_vaccine)
```



